Cloud-assisted collaborative execution for mobile applications with general task topology

Abstract

Mobile cloud computing has been touted as an effective solution to extend the capabilities of resource-poor mobile devices for executing computation intensive applications. In this paper, we investigate cloud-assisted collaborative execution for mobile applications with general task topology to reduce the energy consumption on mobile devices. A mobile application consists of fine-grained tasks organized in general topology. Each task can be executed either on the mobile device or offloaded to the cloud for execution, which is referred to as collaborative task execution. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically mapping the task execution to the mobile device or to the cloud. We formulate the collaborative task execution as a delay-constrained workflow scheduling problem. For the workflow scheduling, we first leverage partial critical path analysis (PCP) to find out the critical path formed by a set of critical parents, in which the critical parent is defined as the parent node of a task that results in the maximum value of the earliest start time of the task. Then, for each path, we find its sub-deadline and apply one-climb policy to schedule the tasks on the path, in which there exists at most one migration from the mobile device to the cloud if ever for the minimum energy consumption. Simulation results show that the proposed collaborative task execution can save energy consumption compared to the local execution and is more flexible than the remote execution.